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Self-Interacting Proteins (SIPs), whose two or more copies can interact with each other, have significant roles in cellular functions and evolution of Protein Interaction Networks (PINs). Knowing whether a protein can act on itself is important to understand its functions. Previous studies on SIPs have focused on their structures and functions, while their whole properties are less emphasized. Not surprisingly, identifying SIPs is one of the most important works in biomedical research, which will help to understanding the function and mechanism of proteins. It is worth noting that high throughput methods can be used for SIPs prediction, but can be costly, time consuming and challenging. Therefore, it is urgent to design computational models for the identification of SIPs. In this review, the concept and function of SIPs were introduced in detail. We further introduced SIPs data and some excellent computational models that have been designed for SIPs prediction. Specially, the most existing approaches were developed based on machine learning through carrying out different extract feature methods. Finally, we discussed several difficult problems in developing computational models for SIPs prediction.

MicroRNAs (miRNAs) play a key role in many critical biological processes and are involved in the occurrence and development of complex human diseases. Many studies demonstrated that discovering the associations between small molecules (SMs) and miRNAs will facilitate the design of miRNA targeted therapeutic strategies for complex human diseases. This work presents a calculation model of cross-layer dependency inference on multilayered networks for small molecule–miRNA association prediction (CLDISMMA), which constructed multilayered networks composed of SMs, miRNAs, and diseases. It utilized the within layer topology and the known cross-layer associations to infer latent representations of all layers for SM–miRNA association prediction. In CLDISMMA, the novelties lie in introducing disease information for SM–miRNA association prediction and utilizing a regularized optimization model to describe the SM–miRNA association prediction problem. To evaluate the performance of CLDISMMA, global leave-one-out cross validation (LOOCV) and miRNA-fixed and SM-fixed local LOOCV were implemented in two data sets. In data set 1, CLDISMMA achieved AUCs of 0.9889, 0.9886, and 0.7755 in turns. The corresponding AUCs were 0.8726, 0.8798, and 0.7021 based on data set 2. In addition, CLDISMMA obtained average AUCs of 0.9887 and 0.8647 in data sets 1 and 2 under 100 times 5-fold cross validation. Furthermore, we employed CLDISMMA to predict SM–miRNA associations based on data set 1, and 21 out of the top 50 predicted associations were confirmed by experimental reports. In the case study for new SMs, 5-fluorouracil and 5-aza-2′-deoxycytidine, 40 and 30 miRNAs, respectively, were verified to be associated with them among the top 50 miRNAs predicted by CLDISMMA.

As microRNAs (miRNAs) have been reported to be a type of novel high-value small molecule (SM) drug targets for disease treatments, many researchers are engaged in the field of exploring new SM–miRNA associations. Nevertheless, because of the high cost, adopting traditional biological experiments constrains the efficiency of discovering new associations between SMs and miRNAs. Therefore, as an important auxiliary tool, reliable computational models will be of great help to reveal SM–miRNA associations. In this article, we developed a computational model of sparse learning and heterogeneous graph inference for small molecule–miRNA association prediction (SLHGISMMA). Initially, the sparse learning method (SLM) was implemented to decompose the SM–miRNA adjacency matrix. Then, we integrated the reacquired association information together with the similarity information of SMs and miRNAs into a heterogeneous graph to infer potential SM–miRNA associations. Here, the main innovation of SLHGISMMA lies in the introduction of SLM to eliminate noises of the original adjacency matrix to some extent, which plays an important role in performance improvement. In addition, to assess SLHGISMMA’ performance, four different kinds of cross-validations were performed based on two datasets. As a result, based on dataset 1 (dataset 2), SLHGISMMA achieved area under the curves of 0.9273 (0.7774), 0.9365 (0.7973), 0.7703 (0.6556), and 0.9241 ± 0.0052 (0.7724 ± 0.0032) in global leave-one-out cross-validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV, and 5-fold cross-validation, respectively. Moreover, in the case study on three important SMs via removing their known associations, the results showed that most of the top 50 predicted miRNAs were confirmed by the database SM2miR v1.0 or the experimental literature.

More and more studies found that many complex human diseases occur accompanied by aberrant expression of microRNAs (miRNAs). Small molecule (SM) drugs have been utilized to treat complex human diseases by affecting the expression of miRNAs. Several computational methods were proposed to infer underlying associations between SMs and miRNAs. In our study, we proposed a new calculation model of random forest based small molecule–miRNA association prediction (RFSMMA) which was based on the known SM–miRNA associations in the SM2miR database. RFSMMA utilized the similarity of SMs and miRNAs as features to represent SM–miRNA pairs and further implemented the machine learning algorithm of random forest to train training samples and obtain a prediction model. In RFSMMA, integrating multiple kinds of similarity can avoid the bias of single similarity and choosing more reliable features from original features can represent SM–miRNA pairs more accurately. We carried out cross validations to assess predictive accuracy of RFSMMA. As a result, RFSMMA acquired AUCs of 0.9854, 0.9839, 0.7052, and 0.9917 ± 0.0008 under global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV, and 5-fold cross validation, respectively, under data set 1. Based on data set 2, RFSMMA obtained AUCs of 0.8456, 0.8463, 0.6653, and 0.8389 ± 0.0033 under four cross validations according to the order mentioned above. In addition, we implemented a case study on three common SMs, namely, 5-fluorouracil, 17β-estradiol, and 5-aza-2′-deoxycytidine. Among the top 50 associated miRNAs of these three SMs predicted by RFSMMA, 31, 32, and 28 miRNAs were verified, respectively. Therefore, RFSMMA is shown to be an effective and reliable tool for identifying underlying SM–miRNA associations.

ABSTRACTMicroRNAs (miRNAs) play an important role in prevention, diagnosis and treatment of human complex diseases. Predicting potential miRNA-disease associations could provide important prior information for medical researchers. Therefore, reliable computational models are expected to be an effective supplement for inferring associations between miRNAs and diseases. In this study, we developed a novel calculative model named Negative Samples Extraction based MiRNA-Disease Association prediction (NSEMDA). NSEMDA filtered reliable negative samples by two positive-unlabeled learning models, namely, the Spy and Rocchio techniques and calculated similarity weights for ambiguous samples. The positive samples, reliable negative samples and ambiguous samples with similarity weights were used to construct a Support Vector Machine-Similarity Weight model to predict miRNA-disease associations. NSEMDA improved the credibility of negative samples and reduced the impact of noise samples by introducing ambiguous samples with similarity weights to train prediction model. As a result, NSEMDA achieved the AUC of 0.8899 in global leave-one-out cross validation (LOOCV) and AUC of 0.8353 under local LOOCV. In 100 times 5-fold cross validation, NSEMDA obtained an average AUC of 0.8878 and standard deviation of 0.0014. These AUCs are higher than many classical models. Besides, we also carried out three kinds of case studies to evaluate the performance of NSEMDA. Among the top 50 potential related miRNAs of esophageal neoplasms, lung neoplasms and carcinoma hepatocellular predicted by NSEMDA, 46, 50 and 45 miRNAs were verified to be associated with the investigated disease by experimental evidences, respectively. Therefore, NSEMDA would be a reliable calculative model for inferring miRNA-disease associations.

The present study aimed to investigate the effects of sodium butyrate on the intestinal barrier and mast cell activation, as well as inflammatory mediator production, and determine whether mitogen-activated protein kinase signaling pathways are involved in these processes. A total of 72 piglets, weaned at 28 ± 1 d age, were allotted to two dietary treatments (control vs. 450 mg/kg sodium butyrate) for 2 wk. The results showed that supplemental sodium butyrate increased daily gain, improved intestinal morphology, as indicated by greater villus height and villus height:crypt depth ratio, and intestinal barrier function reflected by increased transepithelial electrical resistance and decreased paracellular flux of dextran (4 kDa). Moreover, sodium butyrate reduced the percentage of degranulated mast cells and its inflammatory mediator content (histamine, tryptase, TNF-α and IL-6) in the jejunum mucosa. Sodium butyrate also decreased the expression of mast cell-specific tryptase, TNF-α and IL-6 mRNA. Sodium butyrate significantly decreased the phosphorylated ratio of JNK whereas not affecting the phosphorylated ratios of ERK and p38. The results indicated that the protective effects of sodium butyrate on intestinal integrity were closely related to inhibition of mast cell activation and inflammatory mediator production, and that the JNK signaling pathway was likely involved in this process.

This study was aimed at investigating whether dietary copper/zinc-loaded montmorillonite (Cu/Zn-Mt) could alleviate Escherichia coliLPS-induced intestinal injury through pro- and anti-inflammatory signaling pathways (TLRs, NLRs and TGF-β1) in weaned piglets. Eighteen 21-d-old pigs were randomly divided into three groups (control, LPS and LPS + Cu/Zn-Mt). After 21 d of feeding, pigs in the LPS group and LPS + Cu/Zn-Mt group received i.p. administration of LPS, whereas pigs in the control group received saline. At 4 h post-injection, jejunum samples were collected for analysis. The results indicated that, compared with the LPS group, supplemental Cu/Zn-Mt increased transepithelial electrical resistance, the expressions of anti-inflammatory cytokines (TGF-β1) in mRNA and protein levels, and decreased FD4 and the mRNA expression of pro-inflammatory cytokines (TNF-α, IL-6, IL-8 and IL-1β). The pro-inflammatory signaling pathways results demonstrated that Cu/Zn-Mt supplementation decreased the mRNA levels of TLR4 and its downstream signals (MyD88, IRAK1, TRAF6) but had no effect on NOD1 and NOD2 signals. Cu/Zn-Mt supplementation did not affect NF-κB p65 mRNA abundance, but down-regulated its protein expression. The anti-inflammatory signaling pathways results showed supplemental Cu/Zn-Mt also increased TβRII, Smad4 and Smad7 mRNA expressions. These findings suggested dietary Cu/Zn-Mt attenuated LPS-induced intestinal injury by alleviating intestinal inflammation, influencing TLR4-MyD88 and TGF-β1 signaling pathways in weaned pig.

In this study, cabbage [Brassica rapaL. subsp. chinensis(L.) Hanelt cv. Xinza No 1], mung bean [Vigna radiata(L.) R. Wilczek var. radiatacv. VC‐3762], and wheat (Triticum aestivumL. cv. Altas 66) were grown in Pb‐contaminated soils. Application of ethylenediaminetetraacetic acid (EDTA) (3.0 mmol of EDTA/kg soil) to the soil significantly increased the concentrations of Pb in the shoots and roots of all the plants. Lead concentrations in the cabbage shoots reached 5010 and 4620 mg/kg dry matter on Days 7 and 14 after EDTA application, respectively. EDTA was the best in solubilizing soil‐bound Pb and enhancing Pb accumulation in the cabbage shoots among various chelates (EDTA, diethylenetriaminepentaacetic acid [DTPA], hydroxyethylenediaminetriacetic acid [HEDTA], nitrilotriacetic acid [NTA], and citric acid). Results of the sequential chemical extraction of soil samples showed that the Pb concentrations in the carbonate–specifically adsorbed and Fe–Mn oxide phases were significantly decreased after EDTA treatment. The results indicated that EDTA solubilized Pb mainly from these two phases in the soil. The relative efficiency of EDTA enhancing Pb accumulation in shoots (defined as the ratio of shoot Pb concentration to EDTA concentration applied) was highest when 1.5 or 3.0 mmol EDTA/kg soil was used. Application of EDTA in three separate doses was most effective in enhancing the accumulation of Pb in cabbage shoots and decreased mobility of Pb in soil compared with one‐ and two‐dose application methods. This approach could help to minimize the amount of chelate applied in the field and to reduce the potential risk of soluble Pb movement into ground water.